Cathode Electrolyte Interphase Engineering for Prussian Blue Analogues in Lithium-Ion Batteries.

The increasing use of low-cost lithium iron phosphate cathodes in low-end electric vehicles has sparked interest in Prussian blue analogues (PBAs) for lithium-ion batteries. A major challenge with iron hexacyanoferrate (FeHCFe), particularly in lithium-ion systems, is its slow kinetics in organic electrolytes and valence state inactivation in aqueous ones. We have addressed these issues by developing a polymeric cathode electrolyte interphase (CEI) layer through a ring-opening reaction of ethylene carbonate triggered by OH- radicals from structural water. This facile approach considerably mitigates the sluggish electrochemical kinetics typically observed in organic electrolytes. As a result, FeHCFe has achieved a specific capacity of 125 mAh g-1 with a stable lifetime over 500 cycles, thanks to the effective activation of Fe low-spin states and the structural integrity of the CEI layers. These advancements shed light on the potential of PBAs to be viable, durable, and efficient cathode materials for commercial use.

Early motor skill acquisition in healthy older adults: brain correlates of the learning process.

Motor skill learning is a crucial process at all ages. However, healthy aging is often accompanied by a reduction in motor learning capabilities. This study characterized the brain dynamics of healthy older adults during motor skill acquisition and identified brain regions associated with changes in different components of performance. Forty-three subjects participated in a functional magnetic resonance imaging study during which they learned a sequential grip force modulation task. We evaluated the continuous changes in brain activation during practice as well as the continuous performance-related changes in brain activation. Practice of the motor skill was accompanied by increased activation in secondary motor and associative areas. In contrast, visual and frontal areas were less recruited as task execution progressed. Subjects showed significant improvements on the motor skill. While faster execution relied on parietal areas and was inversely associated with frontal activation, accuracy was related to activation in primary and secondary motor areas. Better performance was achieved by the contribution of parietal regions responsible for efficient visuomotor processing and cortical motor regions involved in the correct action selection. The results add to the understanding of online motor learning in healthy older adults, showing complementary roles of specific networks for implementing changes in precision and speed.

CONTRIBUTION FACTORS OF EFFECTIVE LENS POSITION, TILT, AND DECENTRATION DURING FLANGED SCLERAL FIXATION OF INTRAOCULAR LENS: A Model Eye Study.

PURPOSE: The authors aimed to elucidate the factors related to effective lens position, tilt, and decentration of scleral fixed intraocular lenses (IOLs) with a flanged haptic technique in an artificial eye model using anterior segment optical coherence tomography. METHODS: Two bent 27-gauge needles were passed through a 1.0- or 2.0-mm scleral tunnel, 2.0 mm posterior to the limbus and 180° apart. Both haptics of a three-piece IOL were docked with guide needles and externalized. Factors related to the IOL position were analyzed using anterior segment optical coherence tomography and a stereomicroscope. RESULTS: The 1.0-mm scleral tunnel induced a significantly longer effective lens position than the 2.0-mm tunnel and suture fixation ( P < 0.05 and P < 0.01, respectively). Discrepancy in scleral tunnel length induced higher decentration of the optic to the opposite side of the haptic-embedded shorter tunnel and tilt perpendicular to the fixed axis than that in the scleral tunnel of the same length ( P < 0.001 and P < 0.05, respectively). If the scleral fixation points of both haptics are not exactly 180° apart, the IOL may become decentered and tilted ( P < 0.01 and P < 0.05, respectively). CONCLUSION: In the flanged haptic technique, the length, balance, and position of both scleral tunnels determine IOL effective lens position, tilt, and decentration.

Design Principles for Fluorinated Interphase Evolution via Conversion-Type Alloying Processes for Anticorrosive Lithium Metal Anodes.

Over the past decade, lithium metal has been considered the most attractive anode material for high-energy-density batteries. However, its practical application has been hindered by its high reactivity with organic electrolytes and uncontrolled dendritic growth, resulting in poor Coulombic efficiency and cycle life. In this paper, we propose a design strategy for interface engineering using a conversion-type reaction of metal fluorides to evolve a LiF passivation layer and Li-M alloy. Particularly, we propose a LiF-modified Li-Mg-C electrode, which demonstrates stable long-term cycling for over 2000 h in common organic electrolytes with fluoroethylene carbonate (FEC) additives and over 700 h even without additives, suppressing unwanted side reactions and Li dendritic growth. With the help of phase diagrams, we found that solid-solution-based alloying not only facilitates the spontaneous evolution of a LiF layer and bulk alloy but also enables reversible Li plating/stripping inward to the bulk, compared with intermetallic compounds with finite Li solubility.

Characteristics of perivascular space dilatation in normal aging.

The increased incidence of dilated perivascular spaces (dPVSs) visible on MRI has been observed with advancing age, but the relevance of PVS dilatation to normal aging across the lifespan has yet to be fully clarified. In the current study, we sought to find out the age dependence of dPVSs by exploring changes in different characteristics of PVS dilatation across a wide range of age. For 1220 healthy subjects aged between 18 and 100 years, PVSs were automatically segmented and characteristics of PVS dilatation were assessed in terms of the burden, location, and morphology of PVSs in the white matter (WM) and basal ganglia (BG). A machine learning model using the random forests method was constructed to estimate the subjects' age by employing the PVS features. The constructed machine learning model was able to estimate the age of the subjects with an error of 9.53 years on average (correlation = 0.875). The importance of the PVS features indicated the primary contribution of the burden of PVSs in the BG and the additional contribution of locational and morphological changes of PVSs, specifically peripheral extension and reduced linearity, in the WM to age estimation. Indeed, adding the PVS location or morphology features to the PVS burden features provided an improvement to the performance of age estimation. The age dependence of dPVSs in terms of such various characteristics of PVS dilatation in healthy subjects could provide a more comprehensive reference for detecting brain disease-related PVS dilatation.

Impact of glycemic control on the progression of aortic stenosis: a single-center cohort study using a common data model.

BACKGROUND: Diabetes mellitus (DM) is a well-established risk factor for the progression of degenerative aortic stenosis (AS). However, no study has investigated the impact of glycemic control on the rate of AS progression. We aimed to assess the association between the degree of glycemic control and the AS progression, using an electronic health record-based common data model (CDM). METHODS: We identified patients with mild AS (aortic valve [AV] maximal velocity [Vpeak] 2.0-3.0 m/sec) or moderate AS (Vpeak 3.0-4.0 m/sec) at baseline, and follow-up echocardiography performed at an interval of ≥ 6 months, using the CDM of a tertiary hospital database. Patients were divided into 3 groups: no DM (n = 1,027), well-controlled DM (mean glycated hemoglobin [HbA1c] < 7.0% during the study period; n = 193), and poorly controlled DM (mean HbA1c ≥ 7.0% during the study period; n = 144). The primary outcome was the AS progression rate, calculated as the annualized change in the Vpeak (△Vpeak/year). RESULTS: Among the total study population (n = 1,364), the median age was 74 (IQR 65-80) years, 47% were male, the median HbA1c was 6.1% (IQR 5.6-6.9), and the median Vpeak was 2.5 m/sec (IQR 2.2-2.9). During follow-up (median 18.4 months), 16.1% of the 1,031 patients with mild AS at baseline progressed to moderate AS, and 1.8% progressed to severe AS. Among the 333 patients with moderate AS, 36.3% progressed to severe AS. The mean HbA1c level during follow-up showed a positive relationship with the AS progression rate (ß = 2.620; 95% confidence interval [CI] 0.732-4.507; p = 0.007); a 1%-unit increase in HbA1c was associated with a 27% higher risk of accelerated AS progression defined as △Vpeak/year values > 0.2 m/sec/year (adjusted OR = 1.267 per 1%-unit increase in HbA1c; 95% CI 1.106-1.453; p < 0.001), and HbA1c ≥ 7.0% was significantly associated with an accelerated AS progression (adjusted odds ratio = 1.524; 95% CI 1.010-2.285; p = 0.043). This association between the degree of glycemic control and AS progression rate was observed regardless of the baseline AS severity. CONCLUSION: In patients with mild to moderate AS, the presence of DM, as well as the degree of glycemic control, is significantly associated with accelerated AS progression.

Predicting superagers by machine learning classification based on the functional brain connectome using resting-state functional magnetic resonance imaging.

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology underlying successful aging. We aimed to investigate the unique patterns of functional brain connectome of superagers and develop predictive models to differentiate superagers from typical agers based on machine learning methods. We obtained resting-state functional magnetic resonance imaging (rsfMRI) data and cognitive measures from 32 superagers and 58 typical agers. The accuracies of three machine learning methods including the linear support vector machine classifier (SV), the random forest classifier (RF), and the logistic regression classifier (LR) in predicting superagers were comparable (SV = 0.944, RF = 0.944, LR = 0.944); however, RF achieved the highest area under the curve (AUC; 0.979). An ensemble learning method combining the three classifiers achieved the highest AUC (0.986). The most discriminative nodes for predicting superagers encompassed areas in the precuneus; posterior cingulate gyrus; insular cortex; and superior, middle, and inferior frontal gyrus, which were located in default, salient, and multiple-demand networks. Thus, rsfMRI data can provide high accuracy for predicting superagers, thereby capturing and describing the unique characteristics of their functional brain connectome.

Prussian Blue Nanolayer-Embedded Separator for Selective Segregation of Nickel Dissolution in High Nickel Cathodes.

Transition metal layered oxides (LiNixCoyMn1-x-yO2, NCM) have been considered as one of the most promising cathodes for lithium-ion batteries used in long-mileage electric vehicles and energy storage systems. Despite its potential interest, dissolved transition metal (TM) ions toward anode sides can catalyze parasitic reactions such as electrolytic decomposition and dendritic Li growth, ultimately leading to catastrophic safety hazards. In this study, we demonstrate that Prussian Blue (PB) nanoparticles anchored to a commercial PE separator significantly reduce cell resistance and effectively suppress TM crossover during cycling, even under harsh conditions that accelerate Ni dissolution. Therefore, using a PB-coated separator in a harsh condition to intentionally dissolve Ni2+ ions at a high cutoff potential of 4.6 V, NCM||graphite full cells maintain 50.8% of their initial capacity at the 150th cycle. Scalable production of PB-coated separator through the facile synthetic methods can help establish a new research direction for the design of high-energy-density batteries.

Using Single-Crystal Graphene to Form Arrays of Nanocapsules Enabling the Observation of Light Elements in Liquid Cell Transmission Electron Microscopy.

We have designed and fabricated a TEM (transmission electron microscopy) liquid cell with hundreds of graphene nanocapsules arranged in a stack of two Si3N4-x membranes. These graphene nanocapsules are formed on arrays of nanoholes patterned on the Si3N4-x membrane by focused ion beam milling, allowing for better resolution than for the conventional graphene liquid cells, which enables the observation of light elements, such as atomic structures of silicon. We suggest that multiple nanocapsules provide opportunities for consecutive imaging under the same conditions in a single liquid cell. The use of single-crystal graphene windows offers an excellent signal-to-noise ratio and high spatial resolution. The motion of silicon nanoparticles (a low atomic number (Z) material) interacting with nanobubbles was observed, and analyzed, in detail. Our approach will help advance liquid-phase TEM observations by providing a straightforward method to encapsulate liquid between monolayers of various 2-dimensional materials.

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity.

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).

The structural connectome and motor recovery after stroke: predicting natural recovery.

Stroke patients vary considerably in terms of outcomes: some patients present 'natural' recovery proportional to their initial impairment (fitters), while others do not (non-fitters). Thus, a key challenge in stroke rehabilitation is to identify individual recovery potential to make personalized decisions for neuro-rehabilitation, obviating the 'one-size-fits-all' approach. This goal requires (i) the prediction of individual courses of recovery in the acute stage; and (ii) an understanding of underlying neuronal network mechanisms. 'Natural' recovery is especially variable in severely impaired patients, underscoring the special clinical importance of prediction for this subgroup. Fractional anisotropy connectomes based on individual tractography of 92 patients were analysed 2 weeks after stroke (TA) and their changes to 3 months after stroke (TC - TA). Motor impairment was assessed using the Fugl-Meyer Upper Extremity (FMUE) scale. Support vector machine classifiers were trained to separate patients with natural recovery from patients without natural recovery based on their whole-brain structural connectomes and to define their respective underlying network patterns, focusing on severely impaired patients (FMUE < 20). Prediction accuracies were cross-validated internally, in one independent dataset and generalized in two independent datasets. The initial connectome 2 weeks after stroke was capable of segregating fitters from non-fitters, most importantly among severely impaired patients (TA: accuracy = 0.92, precision = 0.93). Secondary analyses studying recovery-relevant network characteristics based on the selected features revealed (i) relevant differences between networks contributing to recovery at 2 weeks and network changes over time (TC - TA); and (ii) network properties specific to severely impaired patients. Important features included the parietofrontal motor network including the intraparietal sulcus, premotor and primary motor cortices and beyond them also attentional, somatosensory or multimodal areas (e.g. the insula), strongly underscoring the importance of whole-brain connectome analyses for better predicting and understanding recovery from stroke. Computational approaches based on structural connectomes allowed the individual prediction of natural recovery 2 weeks after stroke onset, especially in the difficult to predict group of severely impaired patients, and identified the relevant underlying neuronal networks. This information will permit patients to be stratified into different recovery groups in clinical settings and will pave the way towards personalized precision neurorehabilitative treatment.

The predictive value of lesion and disconnectome loads for upper limb motor impairment after stroke.

OBJECTIVE: The putative effect of lesion-induced brain damage on post-stroke upper limb motor impairment can be estimated by overlaying a patient's lesion or its surrogate with key motor areas. We assessed the predictive value of imaging-based brain damage measures for cross-sectional upper limb motor impairment and subsequent upper limb motor outcome after stroke. METHODS: In 47 stroke patients, upper limb motor impairment was evaluated with the Upper-Extremity Fugl-Meyer Assessment (UE-FMA) at 2 weeks (2W) and 3 months (3M) post-stroke. Given each patient's lesion identified at 2W, we considered the disconnectome, estimated as an ensemble of structural and functional connections passing through the lesion, as a surrogate of the lesion. The lesion load and the disconnectome load were measured by overlaying the lesion and disconnectome with the corticospinal tract (CST) and motor cortex (MC), and their association with the UE-FMA score at 2W and 3M was assessed. RESULTS: Whereas the disconnectome loads on the CST and MC were better in predicting the UE-FMA score at 2W, the lesion load on the CST was better in predicting the UE-FMA score at 3M. Furthermore, when the CST lesion load was combined with the UE-FMA score at 2W, the UE-FMA score at 3M was better predicted, with smaller generalization error, than by using either measure alone. CONCLUSIONS: The combination of the CST lesion load and baseline upper limb motor impairment would provide a tailored fusion of imaging and clinical measures for more accurate motor outcome prediction.

Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke.

BACKGROUND AND PURPOSE: Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate. METHODS: Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not. RESULTS: Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the [Formula: see text] metric to be the sum of the entries in the disconnectome and, more importantly, the [Formula: see text] to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPRLLPR) or showed no difference for [Formula: see text]. CONCLUSIONS: Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network.

Multi-person pose estimation has been gaining considerable interest due to its use in several real-world applications, such as activity recognition, motion capture, and augmented reality. Although the improvement of the accuracy and speed of multi-person pose estimation techniques has been recently studied, limitations still exist in balancing these two aspects. In this paper, a novel knowledge distilled lightweight top-down pose network (KDLPN) is proposed that balances computational complexity and accuracy. For the first time in multi-person pose estimation, a network that reduces computational complexity by applying a "Pelee" structure and shuffles pixels in the dense upsampling convolution layer to reduce the number of channels is presented. Furthermore, to prevent performance degradation because of the reduced computational complexity, knowledge distillation is applied to establish the pose estimation network as a teacher network. The method performance is evaluated on the MSCOCO dataset. Experimental results demonstrate that our KDLPN network significantly reduces 95% of the parameters required by state-of-the-art methods with minimal performance degradation. Moreover, our method is compared with other pose estimation methods to substantiate the importance of computational complexity reduction and its effectiveness.

Selective Ion Sweeping on Prussian Blue Analogue Nanoparticles and Activated Carbon for Electrochemical Kinetic Energy Harvesting.

Kinetic energy is an ideal energy source for powering wearable devices or internet of things (IoTs) because of its abundant availability. Currently, most kinetic energy harvesting systems are based on friction or deformation, which require high-frequency motion or high material durability for sustainable energy harvesting. Here, we introduce selective ion sweeping in a hybrid cell consisting of an ion-adsorbing activated carbon and an ion-hosting Prussian blue analogue nanoparticle for electrochemical kinetic energy harvesting. The flow of electrolyte induced by kinetic motion of the cell causes ion sweeping only on the surface of the supercapacitor and induces current flow between the supercapacitor and the battery electrode. This method exhibits 24.9 µW cm-2 as maximum power of system with 34 Ω load in half-cell test, which is several thousand times smaller than the load used in conventional methods. In a long-term test with full cell, this method supplies a continuous current flow ∼6 µA cm-2 at the flow of 40 mL min-1 for 500 cycles without performance decay. The prototype of the hybrid cell demonstrated kinetic energy harvesting from bare hand press with the various flow speeds from 0.41 to 1.39 cm s-1 as well as walking, running, and door closing, which are representative examples of low-frequency kinetic motions in daily life. We believe that the simple structure of the hybrid cell will enable power supply to various applications from miniaturized systems (e.g., IoTs and wearables) to large-scale systems (e.g., ocean wave energy harvesting).

Neuroprotective Effects of Tetrahydrocurcumin against Glutamate-Induced Oxidative Stress in Hippocampal HT22 Cells.

In the central nervous system, glutamate is a major excitable neurotransmitter responsible for many cellular functions. However, excessive levels of glutamate induce neuronal cell death via oxidative stress during acute brain injuries as well as chronic neurodegenerative diseases. The present study was conducted to examine the effect of tetrahydrocurcumin (THC), a major secondary metabolite of curcumin, and its possible mechanism against glutamate-induced cell death. We prepared THC using curcumin isolated from Curcuma longa (turmeric) and demonstrated the protective effect of THC against glutamate-induced oxidative stress in HT22 cells. THC abrogated glutamate-induced HT22 cell death and showed a strong antioxidant effect. THC also significantly reduced intracellular calcium ion increased by glutamate. Additionally, THC significantly reduced the accumulation of intracellular oxidative stress induced by glutamate. Furthermore, THC significantly diminished apoptotic cell death indicated by annexin V-positive in HT22 cells. Western blot analysis indicated that the phosphorylation of mitogen-activated protein kinases including c-Jun N-terminal kinase, extracellular signal-related kinases 1/2, and p38 by glutamate was significantly diminished by treatment with THC. In conclusion, THC is a potent neuroprotectant against glutamate-induced neuronal cell death by inhibiting the accumulation of oxidative stress and phosphorylation of mitogen-activated protein kinases.

Cerebral white matter abnormalities in patients with charcot-marie-tooth disease.

Here, we report the structural evidence of cerebral white matter abnormalities in Charcot-Marie-Tooth (CMT) patients and the relationship between these abnormalities and clinical disability. Brain diffusion tensor imaging (DTI) was performed in CMT patients with demyelinating (CMT1A/CMT1E), axonal (CMT2A/CMT2E), or intermediate (CMTX1/DI-CMT) peripheral neuropathy. Although all patients had normal brain magnetic resonance imaging, all genetic subgroups except CMT1A had abnormal DTI findings indicative of significant cerebral white matter abnormalities: decreased fractional anisotropy and axial diffusivity, and increased radial diffusivity. DTI abnormalities were correlated with clinical disability, suggesting that there is comorbidity of central nervous system damage with peripheral neuropathy in CMT patients. ANN NEUROL 2017;81:147-151.

Inline fiber optic power sensor featuring a variable tap ratio based on a tightly focused femtosecond laser inscription.

We propose and demonstrate an inline fiber optic power sensor (IFPS) resorting to an embedded waveguide tap, which is formed to traverse across the cladding and core of a standard single-mode fiber. The tap was produced via a single-step inscription based on the femtosecond laser direct-writing method. A tightly focused pulsed laser beam has been particularly exploited to suppress the elongation along the laser propagation direction, thereby improving the cross-sectional symmetry of the created tap waveguide. The fabricated fiber optic tap has been stably combined with a photodiode via a compact package. The achieved tap ratio could be tuned from 1.0% to 5.9% at the wavelength of 1550 nm by adjusting the applied laser power, while the induced excess loss was kept below 0.6 dB. The proposed IFPS will be highly suitable for real-time power monitoring in a variety of applications, including optical communication networks and systems.

Interactive effects of seizure frequency and lateralization on intratemporal effective connectivity in temporal lobe epilepsy.

OBJECTIVE: Patients with temporal lobe epilepsy (TLE) show brain connectivity changes in association with cognitive impairment. Seizure frequency and lateralization are 2 important clinical factors that characterize epileptic seizures. In this study, we sought to examine an interactive effect of the 2 seizure factors on intratemporal effective connectivity based on resting-state functional magnetic resonance imaging (rsfMRI) in patients with TLE. METHODS: For rsfMRI data acquired from 48 TLE patients and 45 healthy controls, we applied stochastic dynamical causal modeling to infer effective connectivity between 3 medial temporal lobe (MTL) regions, including the hippocampus (Hipp), parahippocampal gyrus (PHG), and amygdala (Amyg), ipsilateral to the seizure focus. We searched for the effect of the 2 seizure factors, seizure frequency (good vs poor seizure control) and lateralization (left vs right TLE), on connection strengths and their relationship with the level of verbal memory and language impairment. RESULTS: Impairment of verbal memory and language function was mainly affected by seizure lateralization, consistent with preferential involvement of the left MTL in verbal mnemonic processing. For the fully connected model, which was selected as the effective connectivity structure that best explained the observed rsfMRI time series, alterations in connection strengths were primarily influenced by seizure frequency; there was an increase in the strength of the Hipp to PHG connection in TLE patients with poor seizure control, whereas the strength of the Amyg to PHG connection increased in those with good seizure control. Furthermore, the association between connection strength alterations and cognitive impairment was interactively affected by both seizure frequency and lateralization. SIGNIFICANCE: These findings suggest an interactive effect as well as an individual effect of seizure frequency and lateralization on neuroimaging features and cognitive function. This potential interaction needs to be evaluated in the consideration of multiple seizure factors.

Brain regions important for recovery after severe post-stroke upper limb paresis.

Background The ability to predict outcome after stroke is clinically important for planning treatment and for stratification in restorative clinical trials. In relation to the upper limbs, the main predictor of outcome is initial severity, with patients who present with mild to moderate impairment regaining about 70% of their initial impairment by 3 months post-stroke. However, in those with severe presentations, this proportional recovery applies in only about half, with the other half experiencing poor recovery. The reasons for this failure to recover are not established although the extent of corticospinal tract damage is suggested to be a contributory factor. In this study, we investigated 30 patients with chronic stroke who had presented with severe upper limb impairment and asked whether it was possible to differentiate those with a subsequent good or poor recovery of the upper limb based solely on a T1-weighted structural brain scan. Methods A support vector machine approach using voxel-wise lesion likelihood values was used to show that it was possible to classify patients as good or poor recoverers with variable accuracy depending on which brain regions were used to perform the classification. Results While considering damage within a corticospinal tract mask resulted in 73% classification accuracy, using other (non-corticospinal tract) motor areas provided 87% accuracy, and combining both resulted in 90% accuracy. Conclusion This proof of concept approach highlights the relative importance of different anatomical structures in supporting post-stroke upper limb motor recovery and points towards methodologies that might be used to stratify patients in future restorative clinical trials.